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---
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license: mit
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inference: true
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language:
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- en
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metrics:
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- cer
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- wer
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base_model:
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- facebook/deit-base-patch16-224
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- ai4bharat/IndicBART
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pipeline_tag: image-to-text
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tags:
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- text-generation
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- scene-text-recognition
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- text-recognition
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- computer-vision
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- language-model
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---
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# trocr-indic
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This model utilizes the trocr approach to predict the **Indic Texts** from **cropped_images**.
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## Model Details
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The model follows the TrOCR approach of training OCR for Scene Texts. Since, there is scarcity for generalized model for majority of Indian Languages, this model serves it replacement.
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*Courtesty: TrOCR - [original paper](https://huggingface.co/papers/2109.10282)*
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The model is trained for the following languages:
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- Assamese
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- Bengali
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- Gujarati
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- Hindi
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- Kannada
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- Malayalam
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- Marathi
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- Odia
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- Punjabi
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- Telugu
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- Tamil
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### Model Description
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**IMPORTANT**
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Although the model is trained on these languages due to limitations of IndicBART, the model is trained with only Devnagiri Scripts.
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The output is in the following format:
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```
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<LANGUAGE TOKEN> <TEXT TOKENS> <EOS TOKEN>
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```
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The following flowchart gives a better picture on the approach of training and inference regarding this model.
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- **Datasets used:** [IndicSTR12](https://cvit.iiit.ac.in/research/projects/cvit-projects/indicstr)
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- **Developed by:** Aarya Devarla
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- **Model type:** Visio-Lingual Model / Vision-Language Model
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- **License:** mit
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- **Finetuned from model:** deit, indicBART
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### Results
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| Metric | Assamese | Bengali | Gujarati | Hindi | Kannada | Malayalam | Marathi | Odia | Punjabi | Tamil | Telugu |
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|--------|----------|---------|----------|-------|---------|-----------|---------|------|---------|-------|--------|
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| CER | 0.069 | 0.133 | 0.058 | 0.075 | 0.212 | 0.154 | 0.082 | 0.120 | 0.097 | 0.122 | 0.220 |
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| WER | 0.205 | 0.395 | 0.192 | 0.283 | 0.576 | 0.519 | 0.312 | 0.375 | 0.304 | 0.409 | 0.612 |
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Well, the model isn't perfect. But it's a start.
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## Limitations
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The main limitation comes from IndicBART which is primarily trained on IndicTexts.
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### Recommendations
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Since the TrOCR is modular in approach one can just swap out the IndicBART model and train it with new model. Must keep in mind about the preprocessing and outputs.
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